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      ADZE: a rarefaction approach for counting alleles private to combinations of populations

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      1 , * , 1 , 2 , 3 , 1 , 2 , 4
      Bioinformatics
      Oxford University Press

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          Abstract

          Motivation: Analysis of the distribution of alleles across populations is a useful tool for examining population diversity and relationships. However, sample sizes often differ across populations, sometimes making it difficult to assess allelic distributions across groups.

          Results: We introduce a generalized rarefaction approach for counting alleles private to combinations of populations. Our method evaluates the number of alleles found in each of a set of populations but absent in all remaining populations, considering equal-sized subsamples from each population. Applying this method to a worldwide human microsatellite dataset, we observe a high number of alleles private to the combination of African and Oceanian populations. This result supports the possibility of a migration out of Africa into Oceania separate from the migrations responsible for the majority of the ancestry of the modern populations of Asia, and it highlights the utility of our approach to sample size correction in evaluating hypotheses about population history.

          Availability: We have implemented our method in the computer pro-gram ADZE, which is available for download at http://rosenberglab.bioinformatics.med.umich.edu/adze.html.

          Contact: szpiechz@ 123456umich.edu

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          Most cited references16

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          Counting Alleles with Rarefaction: Private Alleles and Hierarchical Sampling Designs

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            Standardized subsets of the HGDP-CEPH Human Genome Diversity Cell Line Panel, accounting for atypical and duplicated samples and pairs of close relatives.

            The HGDP-CEPH Human Genome Diversity Cell Line Panel is a widely-used resource for studies of human genetic variation. Here, pairs of close relatives that have been included in the panel are identified. Together with information on atypical and duplicated samples, the inferred relative pairs suggest standardized subsets of the panel for use in future population-genetic studies.
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              Australia's oldest human remains: age of the Lake Mungo 3 skeleton.

              We have carried out a comprehensive ESR and U-series dating study on the Lake Mungo 3 (LM3) human skeleton. The isotopic Th/U and Pa/U ratios indicate that some minor uranium mobilization may have occurred in the past. Taking such effects into account, the best age estimate for the human skeleton is obtained through the combination of U-series and ESR analyses yielding 62,000+/-6000 years. This age is in close agreement with OSL age estimates on the sediment into which the skeleton was buried of 61,000+/-2000 years. Furthermore, we obtained a U-series age of 81,000+/-21,000 years for the calcitic matrix that was precipitated on the bones after burial. All age results are considerably older than the previously assumed age of LM3 and demonstrate the necessity for directly dating hominid remains. We conclude that the Lake Mungo 3 burial documents the earliest known human presence on the Australian continent. The age implies that people who were skeletally within the range of the present Australian indigenous population colonized the continent during or before oxygen isotope stage 4 (57,000-71,000 years). Copyright 1999 Academic Press.
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1460-2059
                1 November 2008
                8 September 2008
                8 September 2008
                : 24
                : 21
                : 2498-2504
                Affiliations
                1Center for Computational Medicine and Biology, 2Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA, 3Department of Evolutionary Biology, EBC, Uppsala University, SE-752 36 Uppsala, Sweden and 4 Life Sciences Institute, University of Michigan, Ann Arbor, Michigan 48109, USA
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Martin Bishop

                Article
                btn478
                10.1093/bioinformatics/btn478
                2732282
                18779233
                14b1557f-e5a9-4be9-bd2a-bfacb4f3194f
                © 2008 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 1 May 2008
                : 4 September 2008
                : 5 September 2008
                Categories
                Original Papers
                Genetics and Population Analysis

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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